Towards Smart Behavior of Agents in Evacuation Planning Based on Local Cooperative Path Finding

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F19%3A00348035" target="_blank" >RIV/68407700:21240/19:00348035 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-66196-0_14" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-66196-0_14</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Towards Smart Behavior of Agents in Evacuation Planning Based on Local Cooperative Path Finding

Original language description

We address engineering of smart behavior of agents in evacuation problems from the perspective of cooperative path finding (CPF) in this paper.We introduce an abstract version of evacuation problems we call multi-agent evacuation (MAE) that consists of an undirected graph representing the map of the environment and a set of agents moving in this graph. The task is to move agents from the endangered part of the graph into the safe part as quickly as possible. Although the abstract evacuation task can be solved using centralized algorithms based on network flows that are near-optimal with respect to various objectives, such algorithms would hardly be applicable in practice since real agents will not be able to follow the centrally created plan. Therefore we designed a decentralized evacuation planning algorithm called LC-MAE based on local rules derived from local cooperative path finding (CPF) algorithms. We compared LC-MAE with near-optimal centralized algorithm using agent-based simulations in multiple real-life scenarios. Our finding it that LC-MAE produces solutions that are only worse than the optimum by a small factor. Moreover our approach led to important observations about how many agents need to behave rationally to increase the speed of evacuation. A small fraction of rational agents can speed up the evacuation dramatically.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-17966S" target="_blank" >GA19-17966S: intALG-MAPFg: Intelligent Algorithms for Generalized Variants of Multi-Agent Path Finding</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Knowledge Discovery, Knowledge Engineering and Knowledge Management - 11th International Joint Conference (IC3K/KEOD 2020), Revised Selected Papers

ISBN

978-3-030-66195-3

ISSN

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e-ISSN

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Number of pages

20

Pages from-to

302-321

Publisher name

Springer-Verlag

Place of publication

Berlin

Event location

Vídeň

Event date

Sep 17, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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